Senior AI Engineer - Agentic AI and Voice Agents

YAL; Hyderabad; Sur place; INR 3 500 000 - 5 500 000; Plein temps

Résumé du poste

YAL in Hyderabad seeks a Senior AI Engineer to lead the development of agentic AI systems, including LLM agents that plan, call tools and execute end-to-end business tasks. The role covers streaming voice interactions, real-time reasoning, and tool integration across CRM and databases.

You will own architecture, evaluations, and a hands-on path to production, mentoring engineers and guiding releases with measurable performance and safety standards.

Qualifications

• 5+ years of hands-on AI engineering experience.
• Experience shipping LLM agents in production.
• Strong Python and backend skills with production-grade testing.

Responsabilités

• Design and build LLM agents that plan, call tools and complete multi-step business tasks.
• Integrate streaming voice and chat channels with ASR/TTS, WebSockets and real-time data flows.
• Lead architectural decisions, evaluation systems and release-ready tooling for production.
• Mentor engineers and drive production readiness across teams.

Connaissances

Python

Backend development

LLM agents

Tool calling

RAG

WebSockets

Performance optimization

Team leadership

Distributed debugging

Formation

Bachelor’s degree in a technical field

Outils

Docker

Git

CI/CD

Description du poste

Senior AI Engineer - Agentic AI and Voice Agents

5+ years

Hyderabad office

Full-Time

Role overview

We are hiring a senior AI engineer to build and lead agentic AI systems: LLM agents that plan, call tools

and complete real business tasks end to end. You will build agents that hold live phone and web

conversations — streaming speech recognition, reasoning and tool calls, and speech synthesis in real

time. You will stay hands-on while setting technical direction, owning the evaluation system that proves

each agent works before every release, and helping a team of AI engineers move use cases from a demo

to a reliable, observable service that clients trust with their customers.

Key responsibilities

Agentic systems

• Design and build LLM agents that plan, call tools and complete multi-step business tasks across voice, chat and back-office channels.
• Implement task and conversation state, memory and context management, retrieval (RAG) and model routing between fast and more capable models.
• Define tool schemas and permissions, validate every tool call, and add human approval points for high-impact actions.
• Build durable workflows with checkpoints, retries, idempotency keys, timeouts and compensation for failed actions.
• Use multi-agent designs, such as a router with specialist agents, only where they measurably beat a single agent.
• Integrate CRMs, ticketing systems and databases for actions such as complaint registration, classification, routing, status updates and notifications, with warm human handoff.

Voice agents

• Build streaming pipelines that connect telephony (SIP, Twilio, Exotel) and WebRTC audio to ASR, LLMs and TTS over WebSockets.
• Implement voice activity detection, turn detection, barge-in and cancellation so the agent stops talking the moment a caller speaks.
• Set and hold latency budgets per hop (ASR, LLM, TTS, network), with a target of under [1 s] from end of caller speech to first agent audio.
• Adapt agent behaviour for speech: short turns, confirmations before actions, and recovery from mishearing.
• Support multilingual and code-switched calls, including language detection and matching TTS

Evaluation

• Build and maintain golden test sets that define the user goal, persona, expected tool calls and pass criteria for each scenario.
• Run trajectory evals that check each step, tool choice, arguments and order, not just the final answer.
• Run simulated-caller evals for voice: an LLM plays the caller, TTS voices it with accents, noise and interruptions, and the agent is tested end to end over real audio.
• Score task success, tool-call correctness, policy compliance, hallucination, word error rate and latency, using rule checks and LLM judges calibrated against human ratings.
• Gate releases in CI on eval results, block regressions, and turn every confirmed production

Backend and operations

• Write async Python services (FastAPI, asyncio) with structured logging, tracing and metrics for every agent run and conversation.
• Load-test concurrent sessions, find bottlenecks and keep p95 latency and per-task model cost within budget.
• Protect sensitive data: authentication and authorisation, audit trails for agent actions, PII redaction and tenant isolation.
• Ship with Docker and CI using staged releases and rollback, and troubleshoot live agents in production.
• Work with product and delivery teams to turn client workflows into working agent integrations.

Technical leadership

• Own the architecture of agent and voice systems, document key decisions and explain trade-offs to engineers, product and clients.
• Break client requirements into technical designs and delivery milestones, and assign work across the team.
• Review code and designs, and set team standards for testing, evaluation, monitoring and release.
• Mentor AI engineers through pairing, design reviews and feedback, and help them grow into owning features end to end.
• Lead incident response for production issues, run blameless post-mortems and drive the fixes to completion.

Required qualifications and skills

• Strong Python and backend skills: APIs, asyncio, WebSockets, structured error handling and automated tests.
• Hands-on experience shipping LLM agents beyond a prompt demo: tool calling, state management, RAG and failure handling in a real workflow.
• Sound judgement on agent design: when a fixed workflow beats an autonomous agent, single versus multi-agent, and how to keep agents safe with permissions and guardrails.
• Working knowledge of a real-time voice stack: streaming ASR and TTS, VAD, turn-taking and the trade-offs between latency and accuracy.
• Experience designing evaluations for LLM agents: test sets, trajectory and outcome metrics, LLM-as-judge and regression tracking — and knowing when a metric is misleading.
• Ability to debug across services using logs, traces and recorded sessions to find where an agent failed or slowed down.
• Experience with SQL or NoSQL databases, Git, Docker and CI pipelines.
• Evidence of owning production services from design through operation, including reliability under real traffic.
• Experience mentoring engineers, reviewing their work and leading technical decisions with product or client stakeholders.
• Bachelor’s degree in a technical field or equivalent practical experience, with clear examples of delivered work.

Preferred experience

• Agent frameworks and orchestration tools such as LangGraph, OpenAI Agents SDK, Temporal or
• Production voice agents on telephony (SIP, Twilio, Exotel) or WebRTC, with frameworks such as
• Speech providers such as Deepgram, Whisper, Google STT, ElevenLabs, Azure TTS or Cartesia, and measuring their WER and latency on your own data.
• Indian-language and code-switched speech (Hindi, Telugu, Tamil and English).
• Queues, caching and event-driven systems (Redis, Kafka, RabbitMQ), and load-testing tools such as Locust or k6.
• Access controls, PII redaction and on-premises or restricted-network deployments.
• Other modalities, such as vision or document understanding (images, screenshots, scanned forms), and combining them with voice or text in one agent.

Success in the role

• Business outcomes: agents measurably resolve tasks without human help, cut handling time and raise customer satisfaction for client use cases.
• Reliability: workflows recover from timeouts, tool failures and bad input; write actions are never duplicated; every incident is traced to a cause and fixed.
• Evaluation culture: every release is gated by evals, metrics track what users actually experience, and production failures flow back into the test sets.
• Performance and cost: latency and per-task model cost stay within agreed budgets as traffic grows.
• Delivery: client requirements become working integrations on schedule, with clear handover to product and delivery teams.
• Engineering quality: features are tested, observable, documented and easy for others to maintain.
• Team growth: architecture decisions are documented, engineers you mentor own features end to end, and the team ships faster with fewer production issues.